The long-term goal of the proposed research is to develop multi-modality, image-based markers for[unreadable] assessing breast density and parenchyma! structure that may be used alone or together with clinical[unreadable] measures, as well as biomarkers, for use in determining risk of breast cancer. The general hypothesis is[unreadable] that inclusion of automated analyses of the parenchyma will improve the assessment of breast cancer risk.[unreadable] The specific objectives of the proposed research are (1) to perform mage-based categorization of patient[unreadable] databases based on breast density, parenchyma morphology, and parenchyma kinetics [that will be[unreadable] automatically extracted], (2) perform correlation and modeling of the various descriptors of breast density[unreadable] and parenchymal characteristics (i.e. image-markers) with known surrogate markers of risk (such as[unreadable] BRCA1 and BRCA2 heterozygotes and presence of cancer on the contralateral breast) to yield new imagebased[unreadable] markers of risk, (3) perform correlation of the various descriptors of breast density and parenchymal[unreadable] characteristics (i.e. image-markers) with developing biomarkers and candidate genes to yield better[unreadable] understanding of breast cancer risk, and (4) perform preclinical assessment and translation of the density[unreadable] and parenchymal characteristics of women at high risk using these new models. This clinical translational[unreadable] component will involve quantitative comparison with the current method of risk assessment of the Gail[unreadable] model and a case control study with databases from other institutions relating the image-based markers to[unreadable] onset of cancer. In the future, it is expected that such image-based markers will be useful for improved[unreadable] assessment of patients at high risk for breast cancer and for monitoring the response of preventive[unreadable] treatments. The proposed research is novel in that other correlative research in breast cancer risk with[unreadable] image-based analyses involves only breast density. However, here we incorporate two additional,[unreadable] potentially complementary, analyses of the breast parenchyma into the correlative and modeling research.[unreadable] The University of Chicago is extremely well-positioned to perform this correlative research on multimodality[unreadable] image-based analyses for breast cancer risk because of its 20-year history of developing multimodality[unreadable] computer-aided diagnosis methods for mammography, sonography, and MRI, and its integration[unreadable] with the University of Chicago Cancer Risk Clinic.